





'''simple docstring'''






import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def   __A    (				a_    :	Dataset   ,a_    :	Dict[str, str]						):
		lowerCAmelCase  :     str       						= args.log_outputs
		lowerCAmelCase  :     Tuple       						= "_".join(args.dataset.split("/"						) + [args.config, args.split]						)

		# load metric
		lowerCAmelCase  :     List[Any]       						= load_metric("wer"						)
		lowerCAmelCase  :     str       						= load_metric("cer"						)

		# compute metrics
		lowerCAmelCase  :     str       						= wer.compute(references=result["target"]   ,predictions=result["prediction"]						)
		lowerCAmelCase  :     Optional[int]       						= cer.compute(references=result["target"]   ,predictions=result["prediction"]						)

		# print & log results
		lowerCAmelCase  :     Dict       						= f'''WER: {wer_result}\nCER: {cer_result}'''
		print(a_						)

		with open(f'''{dataset_id}_eval_results.txt'''   ,"w"						) as f:
				f.write(a_						)

		# log all results in text file. Possibly interesting for analysis
		if log_outputs is not None:
				lowerCAmelCase  :     int       						= f'''log_{dataset_id}_predictions.txt'''
				lowerCAmelCase  :     Optional[int]       						= f'''log_{dataset_id}_targets.txt'''

				with open(a_   ,"w"						) as p, open(a_   ,"w"						) as t:
						# mapping function to write output
						def write_to_file(a_    :	Tuple   ,a_    :	List[str]						):
								p.write(f'''{i}''' + "\n"						)
								p.write(batch["prediction"] + "\n"						)
								t.write(f'''{i}''' + "\n"						)
								t.write(batch["target"] + "\n"						)

						result.map(a_   ,with_indices=a_						)






def   __A    (				a_    :	str						):
		lowerCAmelCase  :     List[Any]       						= "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		lowerCAmelCase  :     Union[str, Any]       						= re.sub(a_   ,""   ,text.lower()						)

		# In addition, we can normalize the target text, e.g. removing new lines characters etc...
		# note that order is important here!
		lowerCAmelCase  :     Any       						= ["\n\n", "\n", "   ", "  "]

		for t in token_sequences_to_ignore:
				lowerCAmelCase  :     str       						= " ".join(text.split(a_						)						)

		return text






def   __A    (				a_    :	Dict						):
		# load dataset
		lowerCAmelCase  :     Union[str, Any]       						= load_dataset(args.dataset   ,args.config   ,split=args.split   ,use_auth_token=a_						)

		# for testing: only process the first two examples as a test
		# dataset = dataset.select(range(10))

		# load processor
		lowerCAmelCase  :     Optional[int]       						= AutoFeatureExtractor.from_pretrained(args.model_id						)
		lowerCAmelCase  :     List[str]       						= feature_extractor.sampling_rate

		# resample audio
		lowerCAmelCase  :     str       						= dataset.cast_column("audio"   ,Audio(sampling_rate=a_						)						)

		# load eval pipeline
		if args.device is None:
				lowerCAmelCase  :     Tuple       						= 0 if torch.cuda.is_available() else -1
		lowerCAmelCase  :     Optional[Any]       						= pipeline("automatic-speech-recognition"   ,model=args.model_id   ,device=args.device						)

		# map function to decode audio
		def map_to_pred(a_    :	str						):
				lowerCAmelCase  :     List[Any]       						= asr(
				    batch["audio"]["array"]   ,chunk_length_s=args.chunk_length_s   ,stride_length_s=args.stride_length_s						)

				lowerCAmelCase  :     Optional[Any]       						= prediction["text"]
				lowerCAmelCase  :     Optional[Any]       						= normalize_text(batch["sentence"]						)
				return batch

		# run inference on all examples
		lowerCAmelCase  :     Tuple       						= dataset.map(a_   ,remove_columns=dataset.column_names						)

		# compute and log_results
		# do not change function below
		log_results(a_   ,a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()

					parser.add_argument(
					    """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
					)
					parser.add_argument(
					    """--dataset""",
					    type=str,
					    required=True,
					    help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
					)
					parser.add_argument(
					    """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
					)
					parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
					parser.add_argument(
					    """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
					)
					parser.add_argument(
					    """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
					)
					parser.add_argument(
					    """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
					)
					parser.add_argument(
					    """--device""",
					    type=int,
					    default=None,
					    help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
					)
					lowerCAmelCase									=   parser.parse_args()

					main(args)










'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



